Trip data from July and November 2016 in Hamilton Ontario were compared in order to understand changes in ridership patterns. The following research takes into consideration the number of trips that looked like daily commutes vs. leisure, most active and frequently paired hubs, and compares details such as average distance, duration, and areas through which riders pass. A visualization of patterns between those two months was created in Mapbox. The following research demonstrated 1) a noticeable shift in ridership concentration from Hamilton center to McMaster University campus 2) that July experienced more than twice the amount of leisure trips than November whereas November experience a higher share of commutes 3) that there is an indirect relationship between hub activity and distance to Hamilton’s designated bikeways.
On average, trips were about 4.3 minutes longer in July than they were in November and about .56 km longer. The plot below overlays hourly start counts and demonstrates that in November, significantly fewer riders started trips after 17:00 in November as comared to July. The only hour which experienced more total rides was 8:00. In July, rides peaked at 17:00 whereaas in November, rides peaked at 16:00.
| Â | July | November |
|---|---|---|
| distance (km) | 2.34 | 1.78 |
| duration (m) | 18.3 | 14 |
Approximately 10% of start and end points fell outside of hub geofences and therefore were not assigned to a particular hub in the feed. In order for this valuable data not to be lost, a fishnet grid of uniform polygons was created to sample the study area. The difference between starts per location over the two months was analyzed. Below, we can see from the first row, which corresponds to trip starts not assigned a hub, that 10.83% of trips in July and 10.69% of trips in November began outside of hub geofences. This indicates that there was no significant difference between months in terms of riders starting outside of geofences.
| hub | freq_j | %total_j | freq_n | %total_n | diff |
|---|---|---|---|---|---|
| 4013 | 10.83 | 3035 | 10.69 | 978 | |
| 270 Sherman | 7 | 0.02 | 29 | 0.1 | -22 |
| 40 Oxford - 54 | 281 | 0.76 | 188 | 0.66 | 93 |
| Aberdeen at Queen - 48 | 238 | 0.64 | 159 | 0.56 | 79 |
| Aberdeen at Studholme - 37 | 136 | 0.37 | 50 | 0.18 | 86 |
| Ainslie at Emerson - 25 | 162 | 0.44 | 266 | 0.94 | -104 |
After comparing normalizing trip starts by total trip (%total_j, %total_n) we find an interesting difference: some hubs in November captured a greater percentage of ridershare, many of which fell within the McMaster Univeristy Campus:
| hub | freq_j | %total_j | freq_n | %total_n | diff |
|---|---|---|---|---|---|
| McMaster Student Centre - 17 | 896 | 2.42 | 1492 | 5.25 | -596 |
| McMaster Health Sciences - 10 | 723 | 1.95 | 1100 | 3.87 | -377 |
| McMaster Emerson - 15 | 441 | 1.19 | 614 | 2.16 | -173 |
| Main at Columbia College - 28 | 260 | 0.7 | 544 | 1.92 | -284 |
| Sanders at Hollywood - 13 | 329 | 0.89 | 489 | 1.72 | -160 |
| McMaster Arthur Bourns - 14 | 207 | 0.56 | 435 | 1.53 | -228 |
| McMaster Mary Keyes - 16 | 244 | 0.66 | 433 | 1.52 | -189 |
| King at Cline - 18 | 221 | 0.6 | 367 | 1.29 | -146 |
| Sanders at Binkley - 12 | 217 | 0.59 | 354 | 1.25 | -137 |
| McMaster Stadium - 11 | 70 | 0.19 | 316 | 1.11 | -246 |
| Forsyth at Sterling | 186 | 0.5 | 312 | 1.1 | -126 |
| Ainslie at Emerson - 25 | 162 | 0.44 | 266 | 0.94 | -104 |
| Sterling at Whitton - 19 | 67 | 0.18 | 204 | 0.72 | -137 |
This makes sense because school is not in session during the summer. Overall, the university hubs captured 16.54% of the total ridership starts in November - more than double than in July when that figure was only 7.47 %.
We can see see that 23 hubs out of 119 had more starts in November than in July, indicated in red on the density plot. These indicate important shifts in rider behavior as by default, if everything stayed the same, the number of starts per station should be less than in July by a ratio of 37:28.
nrow(data[data$diff < 0, ])
## [1] 23
Above are the 23 hubs (colored red) that experienced increased activity in November as comapred with July. The base layer is the urban extent of Hamilton. This phenomenon can also be seen by adding the contour density layers to the webmap .
In order to identify possible commutes, we assumed:
It was observed that in July, 1300 trips met the criteria for a commute and in November, 1274 trips met the criteria.
It should be noted that the most common destination in both months was ths the hub 707 - the McMaster Student Center
| Â | July | November |
|---|---|---|
| total | 1300 | 1274 |
| % total | 3.5 | 4.5 |
| distance(km) | 2.03 | 1.95 |
| unique ids | 267 | 233 |
| duration(m) | 9.9 | 9.3 |
| top pair (hub ID) | 551 - 707- 551 | 536 - 707 - 536 |
| total top pair | 31 | 24 |
Limitations: this method is excellent for identifying trips by unique users that go to and from point A and point B in one day, but there are several disadvantages:
The difficulty of identifying leisure trips lies in the fact that we do not know the intent of the user. In this study, the assumption made was that if a trip started and ended at the same hub, it could not constitute any part of a commute. If we only consider these trips, they made up more than twice the share of total trips in July as compared with November.
| Â | July | November |
|---|---|---|
| start = end | 3992 | 1487 |
| total | 37069 | 28401 |
| % | 10.8 | 5.2 |
Now we are left with more than half of trips in July and Novmeber unaccounted for as they do not fit our strict criteria of commutes and leisure. It is very probable that a large share of these trips fall into the category of either one-way commutes or leisure, but further research is needed to classify them. Landuse and/or parcel data would further help to pinpoint where riders live and where they work or go for recreation.
The overlay of the tracks per grid (see web map) showed an interesting phenomenon: tracks often aligned with Hamilton’s bikeways. Bikeways are aimed at making cnnections between residential areas, areas of employment, and recreational amenities. The most popular routes were almost always found on this network of bike paths. This could serve as a guildine for locating stations in the the future.
In order to find out if there is a relationship between hub distance to the nearest bikeway and hub activity, the closest distances between hubs and the bikeway network were calcualted in PostGIS using ST_Distance()
## hub dist
## 1 Gore Park - 74 15.48
## 2 James North at Mulberry - 76 127.59
## 3 Seedworks - 81B 79.50
## 4 King at Millers - 3 334.06
## 5 Cootes at King - 4 113.03
## 6 Cootes at Dundas - 5 5.15
A indirect relationship was found between distance and hub activity. The regression shows that on average for every two meters moved away from a bikeway, a hub lost approximately one trip.
##
## Call:
## lm(formula = activity ~ distance)
##
## Coefficients:
## (Intercept) distance
## 545.9119 -0.5484
This research demonstrated patterns in the ridership behavior in July and November in Hamilton, Ontario: